The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only distinguish pure human/LLM text or collaborative text at best. This remains insufficient for the nuanced regulation, as the LLM-polished human text and humanized LLM text often trigger different policy consequences. In this paper, we explore fine-grained LLM-generated text detection under a rigorous four-class setting. To handle such complexities, we propose RACE (Rhetorical Analysis for Creator-Editor Modeling), a fine-grained detection method that characterizes the distinct signatures of creator and editor. Specifically, RACE utilizes Rhetorical Structure Theory to construct a logic graph for the creator's foundation while extracting Elementary Discourse Unit-level features for the editor's style. Experiments show that RACE outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation.

翻译：大型语言模型的误用需要对合成文本进行精确检测。现有工作主要遵循二元或三元分类设置，最多只能区分纯人类/LLM文本或合作文本，这对于细致监管仍显不足，因为经LLM润色的人类文本和人性化的LLM文本往往触发不同的政策后果。在本文中，我们探索了在严格的四类设置下进行细粒度LLM生成文本检测。为应对此类复杂性，我们提出RACE（面向创作者-编辑者建模的修辞分析），一种通过刻画创作者和编辑者不同特征痕迹的细粒度检测方法。具体而言，RACE利用修辞结构理论构建创作者基础文本的逻辑图，同时提取基本语篇单元级特征以捕捉编辑者风格。实验表明，RACE在识别细粒度类型时优于12个基线方法，且误报率低，为LLM监管提供了政策对齐的解决方案。